diff --git a/fluid/neural_machine_translation/transformer/util.py b/fluid/neural_machine_translation/transformer/util.py
index 77dc3868d894d485b59b67d5d75f5a08a53dbb3c..190abf92f4f48bfc943bd99bf61a222cc6c9d2f0 100644
--- a/fluid/neural_machine_translation/transformer/util.py
+++ b/fluid/neural_machine_translation/transformer/util.py
@@ -17,35 +17,6 @@ _ALPHANUMERIC_CHAR_SET = set(
         unicodedata.category(six.unichr(i)).startswith("N")))
 
 
-def tokens_to_ustr(tokens):
-    """
-    Convert a list of tokens to a unicode string.
-    """
-    token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
-    ret = []
-    for i, token in enumerate(tokens):
-        if i > 0 and token_is_alnum[i - 1] and token_is_alnum[i]:
-            ret.append(u" ")
-        ret.append(token)
-    return "".join(ret)
-
-
-def subtoken_ids_to_tokens(subtoken_ids, vocabs):
-    """
-    Convert a list of subtoken(wordpiece) ids to a list of tokens.
-    """
-    concatenated = "".join(
-        [vocabs.get(subtoken_id, u"") for subtoken_id in subtoken_ids])
-    split = concatenated.split("_")
-    ret = []
-    for t in split:
-        if t:
-            unescaped = unescape_token(t + "_")
-            if unescaped:
-                ret.append(unescaped)
-    return ret
-
-
 def unescape_token(escaped_token):
     """
     Inverse of encoding escaping.
@@ -65,9 +36,33 @@ def unescape_token(escaped_token):
     return _UNESCAPE_REGEX.sub(match, trimmed)
 
 
-def subword_ids_to_str(ids, vocabs):
+def subtoken_ids_to_str(subtoken_ids, vocabs):
     """
     Convert a list of subtoken(word piece) ids to a native string.
     Refer to SubwordTextEncoder in Tensor2Tensor. 
     """
-    return tokens_to_ustr(subtoken_ids_to_tokens(ids, vocabs)).decode("utf-8")
+    subtokens = [vocabs.get(subtoken_id, u"") for subtoken_id in subtoken_ids]
+
+    # Convert a list of subtokens to a list of tokens.
+    concatenated = "".join([
+        t if isinstance(t, unicode) else t.decode("utf-8") for t in subtokens
+    ])
+    split = concatenated.split("_")
+    tokens = []
+    for t in split:
+        if t:
+            unescaped = unescape_token(t + "_")
+            if unescaped:
+                tokens.append(unescaped)
+
+    # Convert a list of tokens to a unicode string (by inserting spaces bewteen
+    # word tokens).
+    token_is_alnum = [t[0] in _ALPHANUMERIC_CHAR_SET for t in tokens]
+    ret = []
+    for i, token in enumerate(tokens):
+        if i > 0 and token_is_alnum[i - 1] and token_is_alnum[i]:
+            ret.append(u" ")
+        ret.append(token)
+    seq = "".join(ret)
+
+    return seq.encode("utf-8")